ST4.2

During geomagnetic storms, the space environment can be drastically altered as the plasma in the upper atmosphere, or ionosphere, moves globally. This plasma redistribution is mainly caused by storm-time electric fields, but another important driver of the velocity of the ions in the plasma is the neutral winds. These winds refer to the movement of the neutral particles that are part of the thermospheric layer of the atmosphere, that can drag the plasma. Geomagnetic storms increase the neutral winds, due to the heating of the thermosphere that comes from the storm. In this study we want to understand how these ionospheric drivers affect the ionosphere behavior because, among other reasons, during geomagnetic storms the plasma can refract and diffract trans-ionospheric signals and, consequently, can cause problems in the navigation systems such as GNSS (Global Navigation Satellite System)/GPS (Global Positioning System) that use the information from the signals.

In this work, our objective is to estimate the electric fields and neutral winds globally during a geomagnetic storm. Global GNSS TEC (total electron content) measurements are ingested by the Ionospheric Data Assimilation 4-Dimensional (IDA4D) algorithm [1], whose output is the electron density rate over a grid at different time steps during a geomagnetic storm. The density rates are treated as “observations” in EMPIRE (Estimating Model Parameters from Ionospheric Reverse Engineering), which is a data assimilation algorithm based on the plasma continuity equation [2,3,4]. Then, the EMPIRE “observations” are used to estimate corrections to the electric field and neutral winds by solving a Kalman filter. To study these drivers with EMPIRE, basis functions are used to describe them. For the global potential field, spherical harmonics are used.

To have a global estimation of the neutral winds, we introduce vector spherical harmonics as the basis function for the first time in EMPIRE. The vector spherical harmonics are used to model orthogonal components of neutral wind in the zonal (east-west) and meridional (north-south) directions. EMPIRE’s Kalman filter needs the error covariance of the vector spherical harmonics decomposition. To calculate it, the basis function is fitted to the model HWM14 (Horizonal Wind Model) values of the neutral winds and the error between the fitting and the model is studied. Later, we study the global potential field and global neutral winds over time to understand how much each driver contributes to the plasma redistribution during the geomagnetic storm on October 25^th 2011. We compare the results to FPI (Fabry-Perot Interferometer) neutral winds measurements to validate the results.   

[1] G.S.Bust, G.Crowley, T.W.Garner, T.L.G.II, R.W.Meggs, C.N.Mitchell, P.S.J.Spencer, P.Yin, and B.Zapfe, Four-dimensional gps imaging of space weather storms, Space Weather, 5 (2007),  doi:10.1029/2006SW000237.

[2] D.S.Miladinovich, S.Datta-Barua, G.S.Bust, and J.J.Makela, Assimilation of thermospheric measurements for ionosphere-thermosphere state estimation, Radio Science, 51 (2016).

[3] D.S.Miladinovich, S.Datta-Barua, A.Lopez, S. Zhang, and G.S.Bust, Assimilation of gnss measurements for estimation of high-latitude convection processes, Space Weather, 18 (2020).

[4] G.S.Bust and S.Datta-Barua, Scientific investigations using ida4d and empire, in Modeling the Ionosphere-Thermosphere System, J. Huba, R. Schunk, and G. Khazanov, eds., John Wiley & Sons, Ltd, 1 ed., 2014.

How to cite: Lopez Rubio, A., Datta-Barua, S., and Bust, G.: Global estimation of ionospheric drivers during extreme storms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15965, https://doi.org/10.5194/egusphere-egu21-15965, 2021.

Real-time assimilative empirical models based on the International Reference Ionosphere (IRI) [1], a 3D quiet-time climatology model of the ionospheric plasma density, provide prompt weather specification by adjusting IRI definitions into a better match with the available measurements and geospace activity indicators [2]. The IRI-based Real-Time Assimilative Model (IRTAM) [3] is one of such Real-Time IRI operational ionospheric weather models based on the low-latency sensor inputs from the Global Ionosphere Radio Observatory (GIRO) [4].

IRTAM leverages predictive properties of the underlying IRI expansion basis formalism [5] that treats dynamics of the ionospheric plasma in terms of its harmonics, both temporal and spatial. It uses Non-linear Error Compensation Technique with Associative Restoration (NECTAR) technique [6] to first detect multi-scale inherent diurnal periodicity of the differences between GIRO measurements and the underlying IRI climatology. Then, under the assumption that variations in time at periodic, planetary-scale Eigen scales (diurnal, half-diurnal, 8-hour, etc.) translate to their spatial properties, it globally interpolates and extrapolates each diurnal harmonic individually. This approach allowed NECTAR to associate observed fragments of the activity at GIRO locations with the unveiling grand-scale weather processes of the matching variability scales, as the ground observatories co-rotate with the Earth.

Predictive properties of IRTAM are discussed in order to establish the baseline predictability of the ionospheric dynamics that analyzes only the latest 24-hour history of its deviation from the expected behavior. Concepts for the next generation empirical forecast models are outlined that would leverage the same principle of associative restoration to evaluate the geospace activity timeline and its subtle associations with subsequent storm-time behavior of the ionosphere.

References

[1] Bilitza, D. (ed.) (1990), International Reference Ionosphere 1990, 155 pages, National Space Science Data Center, NSSDC/WDC-A-R&S 90-22, Greenbelt, Maryland, November 1990.

[2] Bilitza, D., D. Altadill, V. Truhlik, V. Shubin, I. Galkin, B. Reinisch, and X. Huang (2017), International Reference Ionosphere 2016: From ionospheric climate to real-time weather predictions, Space Weather, 15, 418-429, doi:10.1002/2016SW001593.

[3] Galkin, I. A., B. W. Reinisch, X. Huang, and D. Bilitza (2012), Assimilation of GIRO Data into a Real-Time IRI, Radio Sci., 47, RS0L07, doi:10.1029/2011RS004952.

[4] Reinisch, B.W. and I.A. Galkin (2011), Global Ionospheric Radio Observatory (GIRO), Earth Planets Space, vol. 63 no. 4 pp. 377-381, doi:10.5047/eps.2011.03.001

[5] International Telecommunications Union (2009), ITU-R reference ionospheric characteristics, Recommendation P.1239-2 (10/2009). Retrieved from http://www.itu.int/rec/R-REC-P.1239/en.

[6] Galkin, I. A., B. W. Reinisch, A. Vesnin, et al., (2020) Assimilation of Sparse Continuous Near-Earth Weather Measurements by NECTAR Model Morphing, Space Weather, 18, e2020SW002463, doi:10.1029/2020SW002463.

How to cite: Galkin, I., Vesnin, A., Reinisch, B., and Bilitza, D.: Predictability of Ionosphere using Assimilative Empirical Model IRTAM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5718, https://doi.org/10.5194/egusphere-egu21-5718, 2021.

The total electron content (TEC) over the Iberian Peninsula was modeled using a three-step procedure. At the 1^st step the TEC series is decomposed using the principal component analysis (PCA) into several daily modes. Then, the amplitudes of those daily modes is fitted by a multiple linear regression model (MRM) using several types of space weather parameters as regressors. Finally, the TEC series is reconstructed using the PCA daily modes and MRM fitted amplitudes.

The advantage of such approach is that seasonal variations of the TEC daily modes are automatically extracted by PCA. As space weather parameters we considered proxies for the solar UV and XR fluxes, number of the solar flares, parameters of the solar wind and the interplanetary magnetic field, and geomagnetic indices. Different time lags and combinations of the regressors are tested.

The possibility to use such TEC models for forecasting was tested. Also, a possibility to use neural networks (NN) instead of MRM is studied.

How to cite: Morozova, A., Barlyaeva, T., and Barata, T.: Modeling of TEC over the Iberian Peninsula using PCA decomposition and multiple linear regression on space weather parameters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7393, https://doi.org/10.5194/egusphere-egu21-7393, 2021.

The internal component of the geomagnetic field (generated within the Earth's core) is of crucial importance in modulating the impact of space weather events. Although primarily a dipolar field of slowly decreasing intensity, multipolar components can cause changes on interannual time-scales that are important for space weather applications. Of particular importance for space weather application is the location of the auroral oval, the region where it is most likely to see polar auroras. The auroral zone can be defined as a time-averaged auroral oval and it is possible to describe it via the internal geomagnetic field.

To be able to forecast interannual and decadal changes of the auroral oval location can benefit the design of future space missions and the planning of mitigation strategies for countries particularly exposed to severe space weather events (such as the UK).

Here we combine various future evolution scenarios for the geomagnetic field of internal origin with a definition of the auroral zones that rests on the calculation of non-orthogonal, magnetic coordinates. This methodology agrees well with calculations based on more complete magnetospheric and ionospheric physics. We apply our methodology to derive quantitative forecasts for the auroral zones' location over the next decades. 

How to cite: Maffei, S., Livermore, P., and Mound, J.: The future location of auroral zones as described by the geomagnetic field of internal origin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-721, https://doi.org/10.5194/egusphere-egu21-721, 2021.

By causing time variation in Earth's external magnetic field, geomagnetic storms can induce damaging currents in ground-based conducting infrastructure, such as power and communication lines.  The physical link between solar activity and Earth's magnetosphere, while complicated, provides the basis for attempts to forecast geomagnetic storms. Fortunately, we have abundant observational data of both the solar disk and solar wind, which are ameable to the application of data-hungry neural networks to the forecasting problem. To date, almost all neural networks trained for geomagnetic storm forecasting have utilized solar wind observations from the Earth-Sun first Lagrangian point (L1) or closer and have generated deterministic output without uncertainty estimates. Furthermore, existing models generate forecasts for indices that are also sensitive to induced internal magnetic fields, complicating the forecasting problem with another layer of non-linearity. In this work, we present neural networks trained on observations from both the solar disk and the L1 point. Our architecture generates reliable probabilistic forecasts over Est, the external component of the disturbance storm time index, showing that neural networks can learn measures of confidence in their output. 

How to cite: Tasistro-Hart, A., Grayver, A., and Kuvshinov, A.: Probabilistic Geomagnetic Storm Forecasting via Deep Learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10501, https://doi.org/10.5194/egusphere-egu21-10501, 2021.

How to cite: Lang, M., Witherington, J., Turner, H., Owens, M., and Riley, P.: Improving solar wind forecasting using Data Assimilation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2771, https://doi.org/10.5194/egusphere-egu21-2771, 2021.

Geomagnetic indices quantify the disturbance caused by the solar activity in particular regions of the Earth. Among them, the SYM-H and ASY-H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at mid-latitude with a 1-minute resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNN). In this work, we present two DNNs developed to forecast the SYM-H and ASY-H indices. Both networks have been trained using solar wind data from the last two solar cycles and they are able to accurately forecast the indices two hours in advance, considering the solar wind and indices values for the previous 16 hours. The evaluation of both networks reveals a great precision for the forecasting, including good predictions for large storms that occurred during the solar cycle 23.

How to cite: Collado, A., Muñoz, P., and Cid, C.: Deep Neural Networks With Convolutional and LSTM Layers for SYM-H and ASY-H Forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9995, https://doi.org/10.5194/egusphere-egu21-9995, 2021.

Historically, McIntosh classifications of sunspots have been utilised for the prediction of solar flares, with modern day operational flare forecast services still reliant upon these classifications for their predictions. Here, building upon previous Poisson-based flare forecasting models that make use of Mcintosh classifications, a set of various machine learning (ML) techniques are applied to construct a set of new models to predict flares within a 24-hr period.

These ML algorithms are trained and tested using data from a range of independent solar cycle periods, cross-validation techniques are applied and the relative performance of each algorithm is compared. In order to make a direct comparison to Poisson-based forecasts, skill scores are calculated and the performance of each model is presented, results showing that the ML models perform well across multiple metrics. The implications these results have when compared with the previous Poisson-based approach are discussed as well as the problem of solar cycle dependence. Additionally, an exploration of the importance of the individual features (i.e., McIntosh components) on the performance of each prediction model and their physical implications are presented.

How to cite: McCloskey, A., Bloomfield, S., and Gallagher, P.: Sunspot Classifications & Solar Flare Prediction: Does machine learning improve upon Poisson-based prediction models?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10107, https://doi.org/10.5194/egusphere-egu21-10107, 2021.

Trans–ionospheric high frequency (HF) signals experience a strong attenuation following a solar flare, commonly referred to as Short–Wave Fadeout (SWF). Although solar flare-driven HF absorption is a well-known impact of SWF, the occurrence of a frequency shift on radio wave signal traversing the lower ionosphere in the early stages of SWF, also known as "Doppler Flash", is newly reported and not well understood. Some prior investigations have suggested two possible sources that might contribute to the manifestation of Doppler Flash: first,  enhancements of plasma density in the D and lower E regions; second, the lowering of the reflection point in the F region. Observations and modeling evidence regarding the manifestation and evolution of Doppler Flash in the ionosphere are limited. This study seeks to advance our understanding of the initial impacts of solar flare-driven SWF. We use WACCM-X to estimate flare-driven enhanced ionization in D, E, and F-regions and a ray-tracing code (Pharlap) to simulate a 1-hop HF communication through the modified ionosphere. Once the ray traveling path has been identified, the model estimates the Doppler frequency shift along the ray path. Finally, the outputs are validated against observations of SWF made with SuperDARN HF radars. We find that changes in the refractive index due to the F-region's plasma density enhancement is the primary cause of Doppler Flash.

How to cite: Chakraborty, S., Qian, L., Ruohoniemi, J. M., Baker, J., and McInerney, J.: The effects of solar flare-driven ionospheric electron density change on Doppler Flash, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1393, https://doi.org/10.5194/egusphere-egu21-1393, 2021.

The presence of megaelectron-volt (MeV) electrons in the Earth’s outer radiation belt poses a hazardous radiation environment for spaceborne electronics through the total ionization dose effect and deep dielectric charge/discharge phenomenon. Thus, developing a reliable forecasting model for MeV electron events has long been a critical but challenging task for space community. Here we update our recent progresses on the PREdictive model for MEV Electrons (PreMevE). This model exploits the power of machine learning algorithms, takes advantage of the coherence caused by local wave‐electron resonance, and uses electron observations from NOAA POES satellites in low‐Earth orbits as inputs—along with the upstream solar wind speeds and densities and GEO measurements—to provide high‐fidelity 1- and 2-day predictions of 1 MeV, 2 MeV and > 2 MeV electron flux distributions across the whole outer radiation belt. Using near-equatorial long-term electron data from the NASA Van Allen Probes mission, we trained, validated and demonstrated that the PreMevE model has L-shell averaged performance efficiencies of ~0.6 for out-of-sample 1-day forecasts and ~0.5 for 2-day forecasts. This study adds new science significance to an existing LEO and GEO space infrastructure, provides reliable and powerful tools to the whole space community, and also suggests for the development of more future tailored space weather models driven by similar methodologies.

How to cite: Chen, Y., Pires de Lima, R., Sinha, S., and Lin, Y.: PreMevE: A Machine-Learning Based Predictive Model for MeV Electrons inside Earth’s Outer Radiation Belt, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8545, https://doi.org/10.5194/egusphere-egu21-8545, 2021.

Solar storms are hazardous events consisting of a high emission of particles and radiation from the sun that can have adverse effect both in space and on Earth. In particular, the satellites can be damaged by energetic particles through surface and deep dielectric charging. The Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation (PAGER) is an EU Horizon 2020 project, which aims to provide a forecast of satellite charging through a pipeline of algorithms connecting the solar activity with the satellite charging. The plasmasphere modeling is an essential component of this pipeline, as plasma density is a crucial parameter for evaluating surface charging. Moreover, plasma density in the plasmasphere has very significant scientific applications, as it controls the growth of waves and how waves interact with particles. Successful plasmasphere machine learning models have been already developed, using as input several geomagnetic indices. However, in the context of the PAGER project one is constrained to use solar wind features and Kp index, whose forecasts are provided by other components of the pipeline. Here, we develop a machine learning model of the plasma density using solar wind features and the Kp geomagnetic index. We validate and test the model by measuring its performance in particular during geomagnetic storms on independent datasets withheld from the training set and by comparing the model predictions with global images of He+ distribution in the Earth’s plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. Finally, we present the results of both local and global plasma density reconstruction. 

How to cite: Bianco, S., Zhelavskaya, I., and Shprits, Y.: Machine learning model of the plasmasphere to forecast satellite charging caused by solar storms., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15372, https://doi.org/10.5194/egusphere-egu21-15372, 2021.

Enhanced precipitation of magnetospheric energetic particles during substorms increases ionospheric electron density and conductance. Such enhancements, which have timescales of a few hours, are not reproduced by the current ionospheric models. We use linear prediction filter technique to reconstruct the substorm-related response of electron densities at different altitudes and ionospheric conductances from long-term observations made by the European Incoherent SCATer (EISCAT) radar located at Tromso. To characterise the intensity of substorm injection at a 5min time step we use the midlatitude positive bay (MPB) index which basically responds to the substorm current wedge variations. We build response functions (LPF filters) between T0-1h and T0+4hrs (T0 is a substorm onset time) in different MLT sectors to estimate the magnitude and delays of the ionospheric density response at different altitudes. The systematic and largest relative substorm related changes are mostly observed in the lowest part of E and in D regions. The duration of the response is about 3 hours. It starts and reaches maximum magnitude near midnight, from which it mainly propagates toward east, where it decays when passing into the noon-evening sector. Such MLT structure corresponds to the drift motion of the injected high energy electron cloud in the magnetosphere. Model performance is better at the midnight-morning sectors (CC~0.6-0.65), where the response is larger, and it is getting worse at the noon-evening sector (CC~0.3-0.5). We also discuss the changes of effective electron energy spectra with the substorm time and MLT and compare the behaviors of global ionization, auroral absorption and conductance patterns as it propagates azimuthally from midnight along the auroral zone following after T0 time. Research was supported by RFBR grants №19-35-90054 and №19-05-00072 and MON grant №2020-220-08-6949.

How to cite: Stepanov, N., Victor, S., Maria, S., Yasunobu, O., and Xiangning, C.: Substorm related patterns of electron density and conductance changes in the auroral zone., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2469, https://doi.org/10.5194/egusphere-egu21-2469, 2021.

Geomagnetically Induced Currents (GICs) are a space weather hazard that can negatively impact large ground-based infrastructures such as power lines, pipelines, and railways. They are driven by the dynamic spatiotemporal behaviour of currents flowing in geospace, which drive rapid geomagnetic disturbances on the ground. In some cases, geomagnetic disturbances are highly localised and spatially structured due to the dynamical behaviour of geospace currents and magnetosphere-ionosphere (M-I) coupling dynamics, which are complex and often unclear.

In this work, we investigate and quantify the spatial structure of large geomagnetic depressions exceeding several hundred nT according to the 10 strongest events measured over Fennoscandia by IMAGE. Using ground magnetometer measurements we connect these spatially structured geomagnetic disturbances to possible M-I coupling processes and identify their likely magnetospheric origin. In addition, the ability for these disturbances to drive large GICs is assessed by calculating their respective geoelectric fields in Sweden using the SMAP ground conductivity model. To compliment the observations, we also utilise high resolution runs (>7 million cells) of the Space Weather Modeling Framework (SWMF) to determine to what extent global MHD models can capture this behaviour.

How to cite: Dimmock, A., Rosenqvist, L., Viljanen, A., Forsyth, C., Freeman, M., Rae, J., and Yordanova, E.: The quantification and possible sources of spatially structured and large amplitude geomagnetic depressions during strong geomagnetic storms measured by IMAGE, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14395, https://doi.org/10.5194/egusphere-egu21-14395, 2021.

Ground based indices, such as the Dst and AE, have been used for decades to describe the interplay of the terrestrial magnetosphere with the solar wind and provide quantifiable indications of the state of geomagnetic activity in general. These indices have been traditionally derived from ground based observations from magnetometer stations all around the Earth. In the last 7 years though, the highly successful satellite mission Swarm has provided the scientific community with an abundance of high quality magnetic measurements at Low Earth Orbit, which can be used to produce the space-based counterparts of these indices, such the Swarm-Dst and Swarm-AE Indices. In this work, we present the first results from this endeavour, with comparisons against traditionally used parameters, and postulate on the possible usefulness of these Swarm based products for space weather monitoring and forecasting.

How to cite: Papadimitriou, C., Balasis, G., Boutsi, A. Z., Daglis, I. A., Giannakis, O., de Michelis, P., Consolini, G., Gjerloev, J. W., and Trenchi, L.: Indices of geomagnetic activity derived from space-born magnetic data from the Swarm mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2483, https://doi.org/10.5194/egusphere-egu21-2483, 2021.

How to cite: Kervalishvili, G., Matzka, J., Stolle, C., and Rauberg, J.: The open-ended, high cadence, Kp-like geomagnetic Hp30 and Hp60 indices, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2846, https://doi.org/10.5194/egusphere-egu21-2846, 2021.

In situ data from satellites in Low Earth Orbit (LEO) has become indispensable to monitor and explore near-Earth space. In contrast to ground-based observations they provide global coverage, and they sense parameters at altitudes that often remain hidden when applying remote sensing techniques either ground- or space-based. 

In recent years, data derived from instruments onboard LEO missions, which were not primarily dedicated for space science application, have proven added value in deriving the spatial and temporal variations of the magnetosphere, ionosphere and thermosphere.

This presentation will discuss the benefit of calibrated data from platform magnetometers that are originally designed for spacecraft attitude control. We will put focus on the dual-satellite GRACE-FO mission, that is suitable to derive scale-lengths, e.g., for auroral field-aligned currents, and in constellation with data from other platform magnetometers to resolve the local time dependence of the magnetospheric ring current signal. We further introduce new data sets of electron density and GPS-derived topside electron content from the GRACE and GRACE-FO missions.

How to cite: Stolle, C., Xiong, C., and Michaelis, I.: Observing Earth space environment with LEO multi-mission data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8488, https://doi.org/10.5194/egusphere-egu21-8488, 2021.

The European Space Agency (ESA) Swarm mission was launched in November 2013 and consists of three identical satellites flying in near-polar orbits. One satellite is flying at about 515 km, while the other two satellites are flying side-by-side at lower altitudes, starting at 480 km altitude and slowly descending due to atmospheric drag to their current 445 km altitude. This coverage of altitudes, together with the satellite payload that includes an accelerometer and GPS receiver, makes the mission particularly suited for atmospheric density retrieval. Unfortunately, the Swarm accelerometers suffer from several anomalies which limits their usefulness for density retrieval. Currently, only accelerometer observations from one of the lower flying satellites (Swarm-C) can be used to generate high-resolution thermospheric densities. However, all satellites deliver high-quality GPS data and an alternative processing strategy has been developed to derive thermospheric densities from these observations as well.

This presentation describes the processing strategy that is used to derive thermospheric densities from the Swarm accelerometer and GPS observations and presents the latest results. The relatively smooth GPS-derived densities have a temporal resolution of about 20 minutes, and show variations due to solar and geomagnetic activity, as well as seasonal, latitudinal and diurnal variation. For analysis of higher-resolution phenomena, only the accelerometer-derived densities can be used. All Swarm thermospheric densities are available for users at the dedicated ESA Swarm website (ftp://swarm-diss.eo.esa.int), as well as at our thermospheric density database (http://thermosphere.tudelft.nl). This database also includes thermospheric densities for the CHAMP, GRACE and GOCE satellites. For future work, it is planned to further improve the Swarm densities, especially for low solar activity conditions, by including a more sophisticated radiation pressure modelling of the Swarm satellites. In addition, it is planned to extend our database with thermospheric densities for the GRACE-FO mission.

How to cite: van den IJssel, J., Siemes, C., and Visser, P.: Thermospheric densities for the Swarm satellite mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5580, https://doi.org/10.5194/egusphere-egu21-5580, 2021.

The NASA Heliophysics Division Space Weather Science Application (SWxSA) program has as its strategic mission to establish a preeminent space weather capability that supports human and robotic space exploration and meets national, international, and societal needs. This is done by advancing measurement and analysis techniques and expanding knowledge and understanding that improves space weather forecasts and nowcasts. Ultimately, the SWxSA program enables space weather forecasting capability that the Agency and Nation and international community require, in partnership with NASA’s Artemis Program and other Federal agencies, and international partners. This includes the development and launch of missions/instruments that advance our knowledge of space weather and improve its prediction, and the transitioning of technology, tools, models, data, and knowledge from research to operational environments. This presentation will provide an update on NASA’s SWxSA space weather strategy and activities.

How to cite: Spann, J.: NASA Space Weather Science Application Strategy and Activities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8912, https://doi.org/10.5194/egusphere-egu21-8912, 2021.

The H2020 SafeSpace project aims at advancing space weather nowcasting and forecasting capabilities and, ultimately, at contributing to the safety of space assets. This will be achieved through the synergy of five well-established space weather models covering the complete Sun – interplanetary space – Earth’s magnetosphere – radiation belts chain. The combined use of these models will enable the delivery of a sophisticated model of the Van Allen electron belt and of a prototype space weather service of tailored particle radiation indicators. Moreover, it will enable forecast capabilities with a target lead time of 2 to 4 days, which is a tremendous advance from current forecasts that are limited to lead times of a few hours. SafeSpace will improve radiation belt modelling through the incorporation into the Salammbô model of magnetospheric processes and parameters of critical importance to radiation belt dynamics. Furthermore, solar and interplanetary conditions will be used as initial conditions to drive the advanced radiation belt model and to provide the link to the solar origin and the interplanetary drivers of space weather. This approach will culminate in a prototype early warning system for detrimental space weather events, which will include indicators of particle radiation of use to space industry and spacecraft operators. Indicator values will be generated by the advanced radiation belt model and the performance of the prototype service will be evaluated in collaboration with space industry stakeholders. The work leading to this paper has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870437 for the SafeSpace (Radiation Belt Environmental Indicators for the Safety of Space Assets) project.

How to cite: Daglis, I. A., Bourdarie, S., Cueto Rodriguez, J., Darrouzet, F., Lavraud, B., Poedts, S., Sandberg, I., and Santolik, O. and the SafeSpace Team: Improving nowcasting and forecasting of the Sun-to-Belts space weather chain through the H2020 SafeSpace project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13466, https://doi.org/10.5194/egusphere-egu21-13466, 2021.

We present the solar wind forecast pipeline that is being implemented as part of the H2020 SafeSpace project. The Goal of this project is to use several tools in a modular fashion to address the physics of Sun – interplanetary space – Earth’s magnetosphere. This presentation focuses on the part of the pipeline that is dedicated to the forecasting – from solar measurements – of the solar wind properties at the Lagrangian L1 point. The modeling pipeline puts together different mature research models: determination of the background coronal magnetic field, computation of solar wind acceleration profiles (1 to 90 solar radii), propagation across the heliosphere (for regular solar wind, CIRs and CMEs), and comparison to spacecraft measurements. Different magnetogram sources (WSO, SOLIS, GONG, ADAPT) can be combined, as well as coronal field reconstruction methods (PFSS, NLFFF), wind (MULTI-VP) and heliospheric propagation models (CDPP 1D MHD, EUHFORIA). We aim at providing a web-based service that continuously supplies a full set of bulk physical parameters of the solar wind at 1 AU several days in advance, at a time cadence compatible with space weather applications. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870437.

How to cite: Lavraud, B., Pinto, R., Kieokaew, R., Samara, E., Poedts, S., Génot, V., Rouillard, A., Brunet, A., Bourdarie, S., Grison, B., Soucek, J., and Daglis, Y.: Modeling the Sun – Earth propagation of solar disturbances for the H2020 SafeSpace project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10796, https://doi.org/10.5194/egusphere-egu21-10796, 2021.

Space Weather (SW) research is a very important topic from the scientific, operational and civic society point of view. Knowledge of interactions in the Sun-Earth system, the physics behind observed SW phenomena, and its direct impact on modern technologies were and will be key areas of interest.  The LOFAR for Space Weather (LOFAR4SW) project aim is to prepare a novel tool which can bring new capabilities into this domain. The project is realised in the frame of a Horizon 2020 INFRADEV call.  The base for the project is the Low Frequency Array (LOFAR) - the worlds largest low frequency radio telescope, with a dense core near Exloo in The Netherlands and many stations distributed both in the Netherlands and Europe wide with baselines up to 2000 km.  The final design of LOFAR4SW will provide a full conceptual and technical description of the LOFAR upgrade, to enable simultaneous operation as a radio telescope for astronomical research as well as an infrastructure working for Space Weather studies.  In this work we present the current status of the project, including examples of the capabilities of LOFAR4SW and the project timeline as we plan for the Critical Design Review later in 2021.

How to cite: Rothkaehl, H., Matyjasiak, B., Baldovin, C., Bisi, M., Barnes, D., Carley, E., Carozzi, T., Fallows, R. A., Gallagher, P. T., Mevius, M., Robertson, S. C., Ruiter, M., Verbiest, J., Vermeulen, R., and Vilmer, N.: LOFAR4SW – Space Weather Science and Operations with LOFAR, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6455, https://doi.org/10.5194/egusphere-egu21-6455, 2021.

Forecasting solar flares based on while-light images and photospheric magnetograms of sunspots is notoriously challenging, while accurate forecasting of coronal mass ejections (CME) is still in its infancy. That said, the chances of a CME being launched is more likely following a flare. CMEs launched from the western hemisphere and “halo” CMEs are the most likely to be geomagnetically impactful, but forecasting their arrival and impact at Earth depends on how well their velocity is known near the Sun, the solar wind conditions between the Sun and the Earth, the accuracy of theoretical models and on the orientation of the CME magnetic field.  In this presentation, we describe a well observed active region, flare, CME, radio burst and sudden geomagnetic impulse that was observed on December 7-10, 2020 by a slew of instruments (SDO, ACE, DSCOVR, PSP, US and European magnetometers). This was a solar eruption that was not expected, but the CME and resulting geomagnetic impact should have been straight-forward to model and forecast. What can we learn from our failure to forecast this simple event and its impacts at Earth? 

How to cite: Gallagher, P., Murray, S., Malone-Leigh, J., Campanyà, J., Cañizares, A., Carley, E., and Blake, S.: Failure to forecast: A case study in nowcasting and forecasting the eruption of a coronal mass ejection and its geomagnetic impacts on Dec 7-10, 2020., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15520, https://doi.org/10.5194/egusphere-egu21-15520, 2021.

A series of experiences and recommendations are presented concerning the measurement of geomagnetically induced currents (GIC) in the Spanish power transmission grid by use of the method of differential magnetometry under power lines, by which differential observations are made (one below the line and another at a few hundred meters away) using vector magnetometers to capture the magnetic effect of the GIC flowing through them. This indirect technique, aimed at obtaining observations to validate GIC computational models, is an alternative to the more common way of measuring the current flow in the transformer neutrals, as it does not rely on the involved power grid operators. In contrast, the selection of a suitable site devoid of human interferences, the need of power for the magnetometer/acquisition system, and the election of the appropriate instrumentation are difficulties that often require costly solutions. Our methodology includes the settlement of appropriate magnetometers with the correct levelling and orientation placed inside buried water-proof containers. The magnetometers are fed by solar panel-battery systems, and we have also developed low-consumption data-transmission models using Raspberry-Pi with GPRS connection technology. According to our experience, only induced currents above about 1 A give magnetic signatures that exceed the noise threshold. As we started measuring during the solar minimum and Spain is a mid-latitude country, the latter fact limited the significance of available recorded data, but we can already report and analyse the results for a number of minor geomagnetic storms.

How to cite: Torta, J. M., Marsal, S., Curto, J. J., Cid, O., Ibañez, M., Canillas, V., and Marcuello, A.: Validating GIC modelling in the Spanish power grid by differential magnetometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4598, https://doi.org/10.5194/egusphere-egu21-4598, 2021.

We study intense geomagnetic storms (Dst < 100nT) during the first half of the solar cycle 24. This type of storm appeared only a few times, mostly associated with southwardly directed heliospheric magnetic field  B_z . Using various methodology as self-organizing maps, statistical and superposed epoch analysis, we show that during and right after intense geomagnetic storms, growth in the number of transmission lines failures, which might be of solar origin, appeared. We also examine the temporal changes in the number of failures during 2010-2014 and found the growing linear tendency of electrical grid failures occurrence possibly connected with solar activity. We confront these results with the geoelectric field calculated for the Poland region using a 1-D layered conductivity Earth model.

How to cite: Gil, A., Berendt-Marchel, M., Modzelewska, R., Moskwa, S., Siluszyk, A., Siluszyk, M., Tomasik, L., Wawrzaszek, A., and Wawrzynczak, A.: Geoelectric field as a GIC proxy during the intense geomagnetic storms and Polish transmission lines failures occurrence, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11822, https://doi.org/10.5194/egusphere-egu21-11822, 2021.

The long-standing disparity between the sunspot number record and the Hoyt and Schatten (1998, H&S) Group Sunspot Number series was initially resolved by the Clette et al. (2014) revision of the sunspot number and the group number series. The revisions resulted in a flurry of dissenting group number series while the revised sunspot number series was generally accepted. Thus, the disparity persisted and confusion reigned, with the choice of solar activity dataset continuing to be a free parameter. A number of workshops and follow-up collaborative efforts by the community have not yet brought clarity. We review here several lines of evidence that validate the original revisions put forward by Clette et al. (2014) and suggest that the perceived conundrum no longer need to delay acceptance and general use of the revised series. We argue that the solar observations constitute several distinct populations with different properties which explain the various discontinuities in the series. This is supported by several proxies: diurnal variation of the geomagnetic field, geomagnetic signature of the strength of the heliomagnetic field, and variation of radionuclides. The Waldmeier effect shows that the sunspot number scale has not changed over the last 270 years and a mistaken scale factor between observers Wolf and Wolfer explains the disparity beginning in 1882 between the sunspot number and the H&S reconstruction of the group number. Observations with replica of 18th century telescopes (with similar optical flaws) validate the early sunspot number scale; while a reconstruction of the group number with monthly resolution (with many more degrees of freedom) validate the size of Solar Cycle 11 given by the revised series that the dissenting series fail to meet. Based on the evidence at hand, we urge the working groups tasked with producing community-vetted and agreed upon solar activity series to complete their work expeditiously.

How to cite: Svalgaard, L.: Several Populations of Sunspot Group Numbers – Resolving a Conundrum, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-282, https://doi.org/10.5194/egusphere-egu21-282, 2021.

Muon flux intensity modulation (MFIM) recognition is a relevant solar-terrestrial physics problem. The considered MFIM, recorded on the Earth's surface, are caused by extreme heliospheric events – the geoeffective solar coronal mass ejections.

The URAGAN muon hodoscope (MH), developed by NRNU MEPhI, a computerized device that measures the intensities of muon fluxes, is used. In the MH, the number of muons falling per unit time on the MH aperture is calculated for the selected system of zenith and azimuthal angles. MH matrix data time series are formed. In the MH data, there are angular modulations due to the action of the hardware function HF, temporal modulations due to atmospheric disturbances and noise: the values of these modulations significantly exceed the values of MFIM of cosmic origin. This circumstance prevents effective MFIM recognition.

A method for MFIM recognition is proposed, based on the mathematical apparatus of the introduced normalized variation functions for MH matrix data, and focused on overcoming the noted circumstance.

A two-dimensional normalized HF is defined for MH. A quite realistic hypothesis is accepted about the initialiy uniform muon flux intensity distributions on a small reference time interval, where there are no extreme heliospheric events and the corresponding reference MH data do not contain significant MFIMs. The estimation of the two-dimensional normalized HF is carried out on the basis of a multiparameter model and its optimization fit to the reference MH data. In order to reduce noise errors, the estimate of the two-dimensional normalized HF is subjected to two-dimensional filtering and subsequent threshold filtering.

Two-dimensional functions of variations of matrix MH datas with respect to two-dimensional normalized AF are calculated. The normalized variation functions are calculated by dividing the two-dimensional functions of variations of matrix MH data by the two-dimensional normalized HF. MFIM recognition method was tested on model and experimental MH data.

A time series of model matrix MH data containing model MFIM was generated. Testing led to a conclusion that it is possible to recognize MFIM with decreases of about 2-3%. A time series of experimental matrix MH data was generated, in which the model MFIM-containing areas were made. Testing led to a conclusion that it is possible to recognize MFIM with the magnitudes of the decreases almost commensurate with the decreases for the case of model MH data.

The proposed MFIM recognition method based on the normalized variation functions for matrix MH data has a favorable perspective for its application in solving problems of geomagnetic storm early diagnostics.

This work was funded by the Russian Science Foundation (project No.17-17-01215).

How to cite: Chinkin, V., Getmanov, V., Sidorov, R., Gvishiani, A., Dobrovolsky, M., Soloviev, A., Dmitrieva, A., Kovylyaeva, A., Osetrova, N., and Yashin, I.: A method for recognizing the muon flux intensity modulations using the normalized variation functions for the URAGAN hodoscope matrix data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-310, https://doi.org/10.5194/egusphere-egu21-310, 2021.